Disaggregation of SMAP L3 Brightness Temperatures to 9km using Kernel Machines
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In this study, a machine learning algorithm is used for disaggregation of SMAP brightness temperatures (T_B) from 36km to 9km. It uses image segmentation to cluster the study region based on meteorological and land cover similarity, followed by a support vector machine based regression that computes the value of the disaggregated T_B at all pixels. High resolution remote sensing products such as land surface temperature, normalized difference vegetation index, enhanced vegetation index, precipitation, soil texture, and land-cover were used for disaggregation. The algorithm was implemented in Iowa, United States, from April to July 2015, and compared with the SMAP L3_SM_AP T_B product at 9km. It was found that the disaggregated T_B were very similar to the SMAP-T_B product, even for vegetated areas with a mean difference ≤ 5K. However, the standard deviation of the disaggregation was lower by 7K than that of the AP product. The probability density functions of the disaggregated T_B were similar to the SMAP-T_B. The results indicate that this algorithm may be used for disaggregating T_B using complex non-linear correlations on a grid.
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